Growing Reimbursement: The Next Frontier

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Healthcare organizations have been trying to effectively capture reimbursement since they began submitting claims for payment. What reimbursement looks like and where it comes from has changed over the years, but the drivers behind the work have stayed the same. Organizations want to receive the appropriate payment for services so they can cover their expenses and have enough money to meet their primary mission of providing top-notch patient care. It takes substantial resources to deliver reliable, high-quality care based on the latest best practice, and organizations need funds to maintain a qualified labor force, procure cutting-edge technology, and purchase the newest equipment. Without a solid reimbursement strategy, an organization can fall short of revenue goals and slow or stall progress toward mission-critical objectives.

Why is consistent reimbursement so hard?

There are many reasons why an organization might struggle with reimbursement. Healthcare’s funding mechanism is complicated to say the least, and it seems to get more complex every day. Part of this is due to the shift in payment responsibilities from payers to patients, caused by the proliferation of high-deductible health plans. Part is due to the emergence of value-driven payment models that reward quality over quantity and require a more nuanced approach to documenting services rendered. And part is the mounting and evolving payer requirements that stem from efforts to reduce risk and meet the increasing demands of employers to cut healthcare costs. The bottom line is healthcare, unlike other industries, does not follow a point-of-service reimbursement model, where the provider hands the patient a bill after care is complete, runs the patient’s credit card, and everything is settled. It takes a lot of people, documentation, and handoffs to facilitate compensation for the expenses incurred as part of care delivery.

So, what does an effective reimbursement strategy look like amid these challenging dynamics?

This is not an easy question, especially since there’s been no shortage of provider effort to manage all the moving parts associated with reimbursement. However, work to date has often focused on revenue cycle team performance and how staff can be more effective. While this is important, to take reimbursement to the next level, organizations must do more with technology. In other words, they must seize opportunities to automate.

As healthcare is catching up to other industries in implementing basic operational and financial technologies, the industry has access to increasingly larger volumes of data, so organizations can do more with next-generation automation tools. Emerging technology like artificial intelligence (AI) can be a game changer in driving reimbursement. Here are three ways this kind of tool can help.

Improve efficiency. Given the complexity in healthcare, it’s unrealistic to think that staff can complete every process without making a mistake and simultaneously watch for anomalies that could negatively impact reimbursement. Automated solutions, however, can easily handle repetitive tasks without error and pinpoint variances at the same time, raising early warning signs that trigger a response. When these tools incorporate AI, they can function with minimal input from staff. Unlike robotic process automation and rules-based solutions that require someone to be aware of a problem and program a robotic script to automate it, AI learns by combing through large volumes of data and uncovering patterns and outliers—without the need for a discreetly created rule. These solutions free staff time, allowing individuals to use their skills to focus on cases that require their expertise, making the overall function more efficient and reliable.

Reduce threats to patient revenue. With the increasing number of high-deductible health plans, patients are feeling the burden of paying for their healthcare. When they face expensive, elective procedures, they may choose to shop around to see if they can find a better price. Or, they may delay the procedure if they can’t afford it, which depending on the issue’s seriousness, could cause larger, costlier problems down the road. If the patient must have the procedure and it is unbudgeted, the chances of collecting on that encounter drop dramatically, leading to increased bad debt for the hospital. All this turmoil can amplify patient frustration, which if unaddressed, may cause the individual to seek care elsewhere the next time. Automated solutions can help organizations balance the need to improve patient collections with fostering patient satisfaction and engagement. For example, new technologies can automatically check for existing and potential insurance coverage, generate realistic cost estimates, and verify if pre-authorizations are needed. They can also determine a patient’s propensity to pay and highlight the best methods of outreach that would yield success in collecting payment. When taken together, these tools can increase cost transparency and ease the payment process, increasing collection rates and safeguarding the patient financial experience.

Uncover opportunities. One of the most intriguing benefits of the latest technologies is their ability to find “new” revenue. This is money the organization is not currently receiving but should. With AI-based solutions, the machine can see patterns the organization may not have recognized before and find opportunities to compliantly increase reimbursement. Perhaps there is a particular procedure the organization regularly performs. By applying AI, the organization can see if there are any reimbursable charges for services, devices, or supplies consumed during care delivery that it may have missed before generating bills. By finding this “new” money, organizations can further expand margins and increase the amount of resources available to support their missions.

Not every AI-based solution is the same

The value an organization pulls out of AI is directly related to the quality and depth of information used to teach it. The more robust the data source, the better the AI tool can learn and predict. Data sources should include information from many different sizes and types of providers and payers, as well as different kinds of consumers.

Solution outputs also contribute to the technology’s value. Recommendations must be readily available at the point of decision-making. Think of it like a Netflix account: The system uses AI to deliver recommendations for what a user should watch right at the moment they are trying to pick a movie. Similarly, revenue cycle AI tools should deliver meaningful information at the point where the user is making a choice. To be fully integrated into revenue cycle workflow, an AI solution needs to be created by a team of people who understand that workflow and know what and when information is needed to make the best decisions. When automated solutions have both the ability to learn from detailed data and present meaningful insights at the perfect moment, those solutions are well-positioned to help influence outcomes and take reimbursement processes to the next level.